Senior Manager, InfoSec & Risk Data Science

Company Description

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

Job Description

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

LinkedIn’s InfoSec Risk Organization is seeking a strategic and technically adept Senior Manager of Risk Data Science to lead a team focused on quantifying, modeling, and mitigating information security risks across the enterprise. This role is pivotal in advancing LinkedIn’s risk posture through data-driven insights, continuous controls assurance, and scalable risk programs aligned with global regulatory frameworks including ISO 27001, NIST CSF, SOX, DMA (Digital Marketing Act), AI Regulations and the Digital Services Act (DSA).

You will collaborate cross-functionally with engineering, legal, compliance, and product teams to embed risk intelligence into operational workflows and strategic planning. This role is ideal for a leader who thrives at the intersection of data science, security, and governance.

Responsibilities:

• Define the strategic vision and roadmap to empower InfoSec by deepening our understanding of governance, controls, risk, and compliance systems, enabling effective optimizations and data-informed decisions.

• Design and implement automated control monitoring systems to continuously validate security control performance and surface emerging vulnerabilities.

• Develop and operationalize machine learning and statistical models to assess threat likelihood, control effectiveness, and systemic risk exposure.

• Lead risk assessment and maturity initiatives, including executive reporting, audit preparedness, and alignment with security, privacy, AI, and resiliency frameworks.

• Build secure data pipelines, dashboards, and analytics platforms that improve visibility into security posture and support risk-informed decision-making.

• Ensure risk analytics, controls assurance, and reporting meet regulatory, compliance, and internal governance requirements.

• Establish prioritization frameworks and partner alignment to ensure security efforts are focused on the highest-impact risks.

• Analyze large-scale structured and unstructured data, generating insights that strengthen control effectiveness, incident prevention, and customer trust.

• Research, develop, and deploy cutting-edge methodologies that improve systemic risk measurement, forecasting, and security program scalability.
• Partner cross-functionally with InfoSec engineering, product security, TPMs, and business stakeholders to embed risk intelligence into engineering and operational workflows.

• Lead the team to deliver strong results by researching and building cutting edge, leverageable methodologies and solutions.

• Be a role model and professional coach for data scientists and managers. Help them realize their potential by setting clear expectations, openly evaluating performance, upholding accountability, and providing challenges (within and outside their team) to stretch their skills.

• Align with relevant teams on prioritization.

• Build strong collaboration and connective tissue with the other Line of Business Data Science teams and with key horizontal DS functions.
• Represent the organization as an industry thought leader in security analytics, resilience, and risk measurement forums.

Qualifications

Basic Qualifications:

• BS or above degrees in a quantitative field - Computer Science, Operational Research, Statistics, Economics, or related fields
• 7+ years of relevant industry or relevant academic experience in Data Science / Analytics working with data; 2+ years experience in managing a team
• Experience with SQL/Relational databases and data visualization tools
• Experience in applied statistics

Preferred Qualifications:

• 10+ years of relevant work experience

• 7+ years of experience in information security, risk management, or GRC.

• 7+ years of hands-on programming experience (e.g., Python, SQL, Java, Scala).

• Experience with large-scale distrib